Abstract
Interval-censored data often arise naturally in medical, biological, and demographical studies. As a matter of routine, the Cox proportional hazards regression is employed to fit such censored data. The related work in the framework of additive hazards regression, which is always considered as a promising alternative, remains to be investigated. We propose a sieve maximum likelihood method for estimating regression parameters in the additive hazards regression with case II interval-censored data, which consists of right-, left- and interval-censored observations. We establish the consistency and the asymptotic normality of the proposed estimator and show that it attains the semiparametric efficiency bound. The finite-sample performance of the proposed method is assessed via comprehensive simulation studies, which is further illustrated by a real clinical example for patients with hemophilia.
Original language | English |
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Pages (from-to) | 708-730 |
Number of pages | 23 |
Journal | Lifetime Data Analysis |
Volume | 26 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Oct 2020 |
Keywords
- Additive hazards
- Empirical process
- Interval-censored data
- Semiparametric efficiency bound
- Sieve maximum likelihood estimator
- Survival analysis
ASJC Scopus subject areas
- Applied Mathematics